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experience performing experiments on fish or other animals. strong quantitative and data-handling skills (e.g. using R/python or an equivalent) certification in animal welfare (e.g. FELASA), although training
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estimations in humanitarian and public health contexts by developing reproducible, multilingual workflows for social media analysis, building data pipelines in R/Python, and creating open-source tools for text
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signal processing and both time-series and frequency domain analysis of physiological recordings including ECG and nerve recordings. This data is used to investigate the cardiovascular effects of altered
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internal institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences
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for analysis (including wet-sieving sediment samples, picking foraminifera under a microscope, cleaning, and weighing) Scientific data analysis and interpretation, also possibly in collaboration with climate
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responsibilities The PhD Research Fellow will: Contribute to study design and implementation across project activities Collect experimental data (including participant recruitment/testing and hands-on lab work) Lead
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on musculoskeletal health in occupational and community settings. Design and implement cohort-wide studies using questionnaires, interviews, and real-time task observations. Collect multimodal data via motion-capture
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- “Information cues and consumer decision–making in livestream e-commerce”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no more than five years of post
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn